STATISTICAL NLP CLASS:
Kemal Oflazer:
- Overview of NLP (2 hours)
- NLP Applications
- Processing pipeline: Basic steps and how they feed into each other and how they are used by applications
- Morphological Analysis (could be skipped or shortened) (2 hours)
- Introduction to Statistical Models, n-gram language modeling, (2hours)
- Applications to simple sequence problems (tagging English and/or deascifier)
- Morphological Disambiguation (applications to Turkish)
- HMMs (formal treatment (backward-forward + viterbi) + applications to tagging) (2-3 hours)
- CFGs and Probabilistic CFGs (3-4 hours)
- Inside-outside algorithm for training PCFGs
- Parsing with PCFGs
- Machine Translation (MT) (3-4 Hours)
- Brief overview Classical Symbolic MT
- Statistical Machine Translation
- Word-based Models
- Phrase-based Models
- Syntax-based models
- Dealing with Morphology in SMT
Dilek Hakkani-Tur:
- Elements of Information Theory / Advanced Language Modeling and Applications
- Entropy/Perplexity/Mutual Information
- Noisy Channel Model
- Sequence classification / HMM
- Sample classification / Naive Bayes
- Smoothing
- Adaptation
- Named Entity Extraction (NE)
- Using HMM for NE
- Using CRF for NE
- Using Boosting/MaxEnt/SVM for NE
- Spoken Language Understanding (SLU) as Template Filling
- HMM approaches (AT&T vs BBN)
- Hidden Vector State Models
- Latent Semantic Analysis
- Sample-classification based (Boosting/MaxEnt/Decision Trees)
- Summarization
- Greedy Algorithms, MMR
- TextRank/LexRank
- Classification based extractive summarization
- Global Models for Summarization: Linear Programming approaches
- Question Answering
- Spoken Dialog Systems and Dialog Management (DM)
- Dialog Systems
- DM
- Finite State Models
- Agent Models
- Reinforcement Learning
Gokhan Tur
- Topic Classification
- Discriminative classification: SVM/Boosting
- Generative classification: language model, document similarity, vector-space-model
- Feature selection/transformation (LDA)
- Latent semantic indexing
- SLU as Intent Determination
- Semantic Role Labeling
- Robustness to ASR
- Topic Clustering
- K-Means
- Top/Down vs. Bottom/Up
- Topic Segmentation
- HMM
- TextTiling
- Markov Chains
- Sentence Segmentation
- HMM
- CRF
- Hybrid
- Active Learning/Semi-Supervised Learning/Unsupervised Learning/Model Adaptation/Robustness
Full post...
